LucidDreamer is a text-to-3D generation framework for distilling high-fidelity textures and shapes from pretrained 2D diffusion models.

Paper and abstract

The recent advancements in text-to-3D generation mark a significant milestone in generative models, unlocking new possibilities for creating imaginative 3D assets across various real-world scenarios. While recent advancements in text-to-3D generation have shown promise, they often fall short in rendering detailed and high-quality 3D models. This problem is especially prevalent as many methods base themselves on Score Distillation Sampling (SDS). This paper identifies a notable deficiency in SDS, that it brings inconsistent and low-quality updating direction for the 3D model, causing the over-smoothing effect. To address this, we propose a novel approach called Interval Score Matching (ISM). ISM employs deterministic diffusing trajectories and utilizes interval-based score matching to counteract over-smoothing. Furthermore, we incorporate 3D Gaussian Splatting into our text-to-3D generation pipeline. Extensive experiments show that our model largely outperforms the state-of-the-art in quality and training efficiency.

@misc{EnVision2023luciddreamer,
      title={LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching}, 
      author={Yixun Liang and Xin Yang and Jiantao Lin and Haodong Li and Xiaogang Xu and Yingcong Chen},
      year={2023},
      eprint={2311.11284},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

For the most up-to-date information about this model, see the original repository.